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Events for the 4th week of February
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PhD Thesis Proposal - Saghar Talebipour
Tue, Feb 20, 2024 @ 01:30 AM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Proposal - Saghar Talebipour
Committee Members: Nenad Medvidovic (Chair), William G.J. Halfond, Chao Wang, Mukund Raghothaman, Sandeep Gupta
Date: Tuesday, February 20, 2024, 1:30 p.m. - 3:00 p.m. Location: EEB 349
Title: Automated Usage-based Mobile Application Testing via Artifact Reuse
Abstract: Writing and maintaining UI tests for mobile applications is both time-consuming and tedious. While decades of research have led to automated methods for UI test generation, these methods have largely focused on identifying crashes or maximizing code coverage. However, recent studies have emphasized the significance of usage-based tests targeting specific app functionalities and use cases. My research introduces novel automated testing techniques that make use of existing artifacts, such as tests from similar applications or video recordings of app operations. These approaches help us move closer to achieving the goal of automated usage-based testing of mobile applications.Location: Hughes Aircraft Electrical Engineering Center (EEB) - 349
Audiences: Everyone Is Invited
Contact: CS Events
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CSC/CommNetS-MHI Seminar: Yongduan Song
Tue, Feb 20, 2024 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Yongduan Song, Director, Research Institute for Artificial Intelligence | Chair Professor, School of Automation | Chongqing University
Talk Title: Several critical issues in neural network driven control design and analysis
Series: CSC/CommNetS-MHI Seminar Series
Abstract:
Neural networks (NN) and related learning algorithms are crucial components of artificial intelligence. The utilization of neural networks combined with learning algorithms for controller design has become a mainstream direction in the field of intelligent control. This talk will examine the typical NN-driven design approaches and expose several critical issues related to functionality and effectiveness of the NN-based control methods.
Biography:
Professor Yongduan Song is a Fellow of IEEE, Fellow of AAIA, Fellow of International Eurasian Academy of Sciences, and Fellow of Chinese Automation Association. He was one of the six Langley Distinguished Professors at National Institute of Aerospace (NIA), USA and registered professional engineer (USA). He is currently the dean of Research Institute of Artificial Intelligence at Chongqing University. Professor Song is the Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems (TNNLS) and the founding Editor-in-Chief of the International Journal of Automation and Intelligence.
Host: Dr Petros Ioannou, ioannou@usc.edu
More Information: 2024.02.20 Seminar - Yongduan Song.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 132
Audiences: Everyone Is Invited
Contact: Miki Arlen
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CS Colloquium - Krishna Kant Chintalapudi (Microsoft Research Redmond) - "Leveling up Next Gen Xbox User Experience with Neural Networks and Sound"
Tue, Feb 20, 2024 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Krishna Kant Chintalapudi, Principal Researcher, Microsoft Research Redmond (MSR)
Talk Title: Leveling up Next Gen Xbox User Experience with Neural Networks and Sound
Abstract: This talk presents two groundbreaking innovations in enhancing the gaming experience on Next-Gen Xbox platforms - ADR-X (NSDI 2024) and Ekho (SIGCOMM 2023). ADR-X, is a neural network-assisted wireless link rate adaptation technique for compute-constrained embedded gaming devices. It uses a meticulously crafted NN based contextual bandit that leverages existing communication theory domain knowledge. This allows ADR-X to perform at par with state-of-the-art reinforcement learning techniques such as PPO while also running 100× faster. Ekho introduces a novel approach to synchronizing cloud gaming media over the internet - crucial for immersive gameplay. By embedding faint, human-inaudible pseudo-noise markers into game audio and detecting them through player microphones, Ekho accurately measures and compensates for inter-stream delays.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Dr. Krishna Kant Chintalapudi is a Principal Researcher in the Networking Research Group at Microsoft Research Redmond (MSR). His research interests span AI/ML, Networking & Systems, Video Analytics, AR/VR and Internet of Things. He has published more than 50 papers in reputed international conferences and journals which have been cited over 8000 times and he holds over 30 patents granted by USPTO. Krishna graduated from the University of Southern California with a Phd in Computer Science in 2006. Prior to joining MSR, Krishna was a Senior Research Engineer at Bosch Research and Technology Center in Palo Alto, CA, USA.
Host: Ramesh Govindan
Location: Hedco Neurosciences Building (HNB) - 107
Audiences: Everyone Is Invited
Contact: CS Events
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CS Colloquium - Pavithra Prabhakar (Kansas State University) - Safety Analysis of AI-enabled Cyber-Physical Systems (CPS): A Formal Approach
Tue, Feb 20, 2024 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Prof. Pavithra Prabhakar, Kansas State University
Talk Title: Safety Analysis of AI-enabled Cyber-Physical Systems (CPS): A Formal Approach
Abstract: AI-based components have become an integral part of Cyber-Physical Systems enabling transformative functionalities. With the ubiquitous use of Machine Learning components in perception, control and decision making in safety critical application domains such as automotive and aerospace, rigorous analysis of these systems has become imperative toward real-world deployment. In this talk, we will present a formal approach to verifying the safety of AI-enabled CPS. We consider a closed-loop system consisting of a dynamical system model of the physical plant and a neural network model of the perception/control modules and analyze the safety of this system through reachable set computation.
One of the main challenges with reachable set computation of neural network-controlled CPS is the scalability of the methods to large networks and complex dynamics. We present a novel abstraction technique for neural network size reduction that provides soundness guarantees for safety analysis and indicates a promising direction for scalable analysis of the closed-loop system. Specifically, our abstraction consists of constructing a simpler neural network with fewer neurons, albeit with interval weights called interval neural network (INN), which over-approximates the output range of the given neural network. We present two methods for computing the output range analysis problem on the INNs, one by reducing it to solving a mixed integer linear programming problem, and the other a symbolic computation method using a novel data structure called the interval star set. Our experimental results highlight the trade-off between the computation time and the precision of the computed output set. We will discuss other foundational questions on neural network size reduction by exploring the notion of equivalence and approximate equivalence. We will conclude by pointing to ongoing work on incorporating a camera model along with a neural network for perception in the closed-loop system framework.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Pavithra Prabhakar is professor in the department of computer science, and the Peggy and Gary Edwards Chair in Engineering at Kansas State University. She is currently serving the National Science Foundation as a Program Director in the Software and Hardware Foundations Cluster in the Computer and Information Science and Engineering Directorate, where she manages formal methods and verification portfolio. Specifically, she leads the Formal Methods in the Field (FMitF) program, has been a founding program director for the Safe Learning Enabled Systems (SLES) program and is a cognizant program director for the Foundations of Robotics Research (FRR) and the Cyber-Physical Systems (CPS) program.
She obtained her doctorate in computer science and a master's degree in applied mathematics from the University of Illinois at Urbana-Champaign, followed by a CMI postdoctoral fellowship at the California Institute of Technology. Prior to coming to K-State, she spent four years at the IMDEA Software Institute in Spain as a tenure-track assistant professor. She is the recipient of a Marie Curie Career Integration Grant from the European Union (2014), an NSF CAREER Award (2016), an ONR Young Investigator Award (2017), NITW distinguished young alumnus award (2021), and an Amazon Research Award (2022).
Host: Jyotirmoy Deshmukh
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: CS Events
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Interview Success: How to Make a Lasting Impression
Tue, Feb 20, 2024 @ 03:00 PM - 04:00 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
THIS EVENT WILL BE HOSTED HYBRID: IN-PERSON & ONLINE SIMULTANEOUSLY
Increase your preparedness for interviews by attending this professional development Q&A moderated by Viterbi Career Connections staff or Viterbi employer partners.
Zoom link: https://usc.zoom.us/meeting/register/tJYkdOGsrD4tHNx6_NKmjnra86aHqsf2Xpsk
For more information about all workshops, please visit viterbicareers.usc.edu/workshops.Location: Ronald Tutor Hall of Engineering (RTH) - 211
Audiences: All Viterbi
Contact: RTH 218 Viterbi Career Connections
Event Link: https://usc.zoom.us/meeting/register/tJYkdOGsrD4tHNx6_NKmjnra86aHqsf2Xpsk
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Epstein Institute, ISE 651 Seminar Class
Tue, Feb 20, 2024 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Prof. Jay Lee, Mork Family Department of Chemical Engineering and Materials Science, USC Viterbi
Talk Title: Role of Process Systems Engineering in Decarbonization and Energy Transition
Host: Prof. Maged Dessouky
More Information: February 20, 2024.pdf
Location: Social Sciences Building (SOS) - SOS Building, B2
Audiences: Everyone Is Invited
Contact: Grace Owh
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DEN@Viterbi: How to Apply Virtual Info Session
Tue, Feb 20, 2024 @ 05:00 PM - 06:00 PM
DEN@Viterbi, Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
Join USC Viterbi representatives for a step-by-step guide and tips for how to apply for formal admission into a Master's degree or Graduate Certificate program. The session is intended for individuals who wish to pursue a graduate degree program completely online via USC Viterbi's flexible online DEN@Viterbi delivery method. Attendees will have the opportunity to connect directly with USC Viterbi representatives and ask questions about the admission process throughout the session.
WebCast Link: https://uscviterbi.webex.com/weblink/register/r77484ac6f88e1bec0d1bcff5ef8c6c3e
Audiences: Everyone Is Invited
Contact: Corporate & Professional Programs
Event Link: https://uscviterbi.webex.com/weblink/register/r77484ac6f88e1bec0d1bcff5ef8c6c3e
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EiS Communications Hub Drop-In HoursWed, Feb 21, 2024 @ 10:00 AM - 01:00 PM
Viterbi School of Engineering Student Affairs
Workshops & Infosessions
Viterbi Ph.D. students are invited to stop by the EiS Communications Hub for one-on-one instruction for their academic and professional communications tasks. All instruction is provided by Viterbi faculty at the Engineering in Society Program.
Location: Ronald Tutor Hall of Engineering (RTH) - 222A
Audiences: Viterbi Ph.D. Students
Contact: Helen Choi
Event Link: https://sites.google.com/usc.edu/eishub/home?authuser=0
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Recruitment Season Workshop
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Wed, Feb 21, 2024 @ 10:00 AM - 05:00 PM
Viterbi School of Engineering Student Affairs
Student Activity
Whether you're actively job hunting or just thinking about your future plans, visit the Viterbi Learning Program for a recruitment season workshop! Our team is available for resume review, cover letter editing, interview practice, and more - plus a range of tasty snacks to fuel your work!
Location: Ronald Tutor Hall of Engineering (RTH) - 222
Audiences: Everyone Is Invited
Contact: Alex Bronz
Event Link: https://cglink.me/2nB/r395640
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EiS Communications Hub Drop-In Hours
Wed, Feb 21, 2024 @ 10:00 AM - 01:00 PM
Engineering in Society Program
Student Activity
Drop-in hours for writing and speaking support for Viterbi Ph.D. students
Location: Ronald Tutor Hall of Engineering (RTH) - 222
Audiences: Everyone Is Invited
Contact: Helen Choi
Event Link: https://sites.google.com/usc.edu/eishub/home
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EiS Communications Hub Drop-In Hours
Wed, Feb 21, 2024 @ 10:00 AM - 01:00 PM
Engineering in Society Program
Student Activity
Drop-in hours for writing and speaking support for Viterbi Ph.D. students
Location: Ronald Tutor Hall of Engineering (RTH) - 222
Audiences: Everyone Is Invited
Contact: Helen Choi
Event Link: https://sites.google.com/usc.edu/eishub/home
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Alfred E. Mann Department of Biomedical Engineering
Wed, Feb 21, 2024 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Konstantinos Konstantopoulos, Ph.D., William H. Schwarz Professor of Chemical and Biomolecular Engineering The Johns Hopkins University
Talk Title: Cell Mechanosensing and Prognostic Assays in Cancer
Abstract: Cell locomotion is a critical step in the process of cancer metastasis, as it enables cancerous cells dissociating from a primary tumor to navigate through interstitial tissues and ultimately colonize distant organs. Metastasizing cells migrate through three-dimensional (3D) longitudinal channel-like tracks created by various anatomical structures or generated via remodeling of extracellular matrix by cancer-associated stroma cells. This seminar will present a multidisciplinary approach, integrating bioengineering tools with molecular and cell biology techniques to understand cancer cell migration in precisely engineered microenvironments, which recapitulate in vitro the 3D longitudinal channels encountered in vivo. The plasticity of cancer cell migration will be discussed, focusing on how cells sense, adapt, and respond to different physical cues, such as confinement and extracellular fluid viscosity. Moreover, this presentation will outline how our current knowledge on the mechanisms of cell motility has led to the development of a novel microchannel assay capable of distinguishing aggressive from non-aggressive cancer cells for accurate diagnosis, prognosis and precision care of cancer patients.
Biography: Received the Diploma of Chemical Engineering from the National Technical University of Athens, Greece in 1989 and the doctorate in Chemical Engineering from Rice University, Houston, Texas in 1995. After his postdoctoral training in the Institute of Biosciences and Bioengineering at Rice University, he joined the faculty of Chemical and Biomolecular Engineering at Johns Hopkins in 1997, and served as Department Chair from 2008 till 2017. He holds secondary appointments in the Departments of Biomedical Engineering and Oncology. He is Fellow of the American Institute for Medical and Biological Engineering (AIMBE) and of the Biomedical Engineering Society (BMES). His signature research focuses on how cells sense and respond to different physical cues. He is known for deciphering a new mechanism of tumor cell migration in confinement called the Osmotic Engine Model, for identifying extracellular fluid viscosity as a novel physical cue regulating cancer metastasis, and for developing innovative prognostic and diagnostic assays in cancer. He has also discovered key functional selectin ligands involved in tumor cell adhesion to host cells, and characterized biophysically these receptor-ligand interactions at the single-molecule level. He has published over 160 peer-reviewed articles in premier journals such as Nature, Cell, Nature Biomedical Engineering, Science Advances etc. His work has been cited ~13,500 times with an h-index of 66. Eleven of his mentees have launched successful academic careers in premier institutions, whereas another 18 have joined the government or industry and now hold leading appointments. He is currently the PI or MPI on multiple NIH R01 and CDMRP grants.
Host: Peter Wang
Location: Corwin D. Denney Research Center (DRB) - 145
Audiences: Everyone Is Invited
Contact: Carla Stanard
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PhD Thesis Proposal - Qinyi Luo
Wed, Feb 21, 2024 @ 11:00 AM - 12:30 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Proposal - Qinyi Luo
Title: High-Performance Heterogeneity-Aware Distributed Machine Learning Model Training
Committee members: Xuehai Qian (co-chair), Viktor Prasanna (co-chair), Ramesh Govindan, Chao Wang, Salman Avestimehr
Abstract: The increasing size of machine learning models and the ever-growing amount of data result in days or even weeks of time required to train a machine learning model. To accelerate training, distributed training with parallel stochastic gradient descent is widely adopted as the go-to training method. This thesis proposal targets four challenges in distributed training: (1) performance degradation caused by large amount of data transfer among parallel workers, (2) heterogeneous computation and communication capacities in the training devices, i.e., the straggler problem, (3) huge memory consumption during training caused by huge model sizes, and (4) automatic selection of parallelization strategies. The proposal first introduces our work in decentralized training, including system support and algorithmic innovation that strengthen tolerance against stragglers in data-parallel training. Then, an adaptive during-training model compression technique is proposed to reduce the memory consumption of training huge recommender models. In the end, in the aspect of automatic parallelization of training workloads, a novel unified representation of parallelization strategies is proposed, as well as a search algorithm that selects superior parallel settings in the vast search space, and preliminary findings are discussed.
Date and time: Feb 21 11am-12:30pm
Location: EEB 110
Zoom link: https://usc.zoom.us/j/97299158202?pwd=bVlnRVFhTjJlZjVCY1hVNy9yWWE1UT09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 110
Audiences: Everyone Is Invited
Contact: CS Events
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DEN@Viterbi - Online Graduate Engineering Virtual Information Session
Wed, Feb 21, 2024 @ 12:00 PM - 01:00 PM
DEN@Viterbi, Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
Join USC Viterbi School of Engineering for a virtual information session via WebEx, providing an introduction to DEN@Viterbi, our top-ranked online delivery system. Discover the 40+ graduate engineering and computer science programs available entirely online. Attendees will have the opportunity to connect directly with USC Viterbi representatives during the session to discuss the admission process, program details, and the benefits of online delivery.
WebCast Link: https://uscviterbi.webex.com/weblink/register/r82a91c6ad51b03d2e2f6c88d71dcaf50
Audiences: Everyone Is Invited
Contact: Corporate & Professional Programs
Event Link: https://uscviterbi.webex.com/weblink/register/r82a91c6ad51b03d2e2f6c88d71dcaf50
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WIE x TBP: Male Allies in STEM
Wed, Feb 21, 2024 @ 06:30 PM - 07:30 PM
USC Viterbi School of Engineering
Conferences, Lectures, & Seminars
Join WIE and the Tau Beta Pi engineering honor society for our Male Allies in STEM Event!
A panel of women and non-binary science and engineering students and faculty will be sharing their stories about identity and experiences with male allyship, to raise awareness about the challenges of working in male-dominated professions and ways that men can be more effective allies.
All undergraduates and graduate students are welcome, and we'll have free burritos (vegetarian and vegan options available)!Location: Sign into EngageSC to View Location
Audiences: Everyone Is Invited
Contact: Thelma Federico Zaragoza
Event Link: https://engage.usc.edu/WIE/rsvp?id=395816
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KIUEL: Viterbi Talent Show (National Engineers Week)
Wed, Feb 21, 2024 @ 07:00 PM - 09:00 PM
USC Viterbi School of Engineering
Student Activity
Join us for an evening filled with live music from USCâs Musicians Club. Show off your hidden talent and compete for bookstore prizes!
Here is the Google Form to sign up to perform in the talent show: https://forms.gle/mpdfJ1xFFT9Z51hd9Location: Sign into EngageSC to View Location
Audiences: Everyone Is Invited
Contact: Kevin Giang
Event Link: https://engage.usc.edu/viterbi/rsvp?id=395821
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Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute for Electrical & Computer Engineering Joint Seminar Series: Dengwang Tang (USC)
Thu, Feb 22, 2024 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dengwang Tang, University of Southern California
Talk Title: Informed Posterior Sampling Based Algorithms for Markov Decision Processes
Series: Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute for Electrical & Computer Engineering Joint Seminar Series
Abstract: The traditional paradigm of RL often features an agent who learns to control the system only through interaction. However, such a paradigm can be impractical since
learning can be very slow. In many engineering applications, there's often an offline dataset available before the application of the online learning algorithm. We proposed
the informed posterior sampling-based reinforcement learning (iPSRL) to use offline datasets to bootstrap online RL algorithms in both episodic and continuing MDP
learning problems. In this algorithm, the learning agent forms an informed prior with the offline data along with the knowledge about the offline policy that generated the data.
This informed prior is then used to initiate the posterior sampling procedure. Through a novel prior-dependent regret analysis of the posterior sampling procedure, we showed
that when the offline data is informative enough, the iPSRL algorithm can significantly reduce the learning regret compared to the baseline. Based on iPSRL, we then
proposed the more practical iRLSVI algorithm and we showed that in episodic MDP learning problems, it can significantly reduce regret via empirical results.
Biography: Dengwang Tang is currently a postdoctoral researcher at University of Southern California. He obtained his B.S.E in Computer Engineering from University of Michigan,
Ann Arbor in 2016. He earned his Ph.D. in Electrical and Computer Engineering (2021), M.S. in Mathematics (2021), and M.S. in Electrical and Computer Engineering (2018) all
from University of Michigan, Ann Arbor. Before joining USC, he was a postdoctoral researcher at University of California, Berkeley. His research interests involve control
and learning algorithms in stochastic dynamic systems, multi-agent systems, queuing theory, and dynamic games.
Host: Pierluigi Nuzzo
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: CS Events
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NL Seminar -Red Teaming Language Model Detectors with Language Models
Thu, Feb 22, 2024 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Yihan Wang, UCLA
Talk Title: Red Teaming Language Model Detectors with Language Models
Series: NL Seminar
Abstract: REMINDER: This talk will be a live presentation only, it will not be recorded. Meeting hosts only admit guests that they know to the Zoom meeting. Hence, you’re highly encouraged to use your USC account to sign into Zoom. If you’re an outside visitor, please provide your: Full Name, Title and Name of Workplace to (nlg-seminar-host(at)isi.edu) beforehand so we’ll be aware of your attendance. Also, let us know if you plan to attend in-person or virtually. More Info for NL Seminars can be found at: https://nlg.isi.edu/nl-seminar/ The prevalence and strong capability of large language models (LLMs) present significant safety and ethical risks if exploited by malicious users. To prevent the potentially deceptive usage of LLMs, recent works have proposed algorithms to detect LLM-generated text and protect LLMs. In this paper, we investigate the robustness and reliability of these LLM detectors under adversarial attacks. We study two types of attack strategies: 1) replacing certain words in an LLM's output with their synonyms given the context; 2) automatically searching for an instructional prompt to alter the writing style of the generation. In both strategies, we leverage an auxiliary LLM to generate the word replacements or the instructional prompt. Different from previous works, we consider a challenging setting where the auxiliary LLM can also be protected by a detector. Experiments reveal that our attacks effectively compromise the performance of all detectors in the study with plausible generations, underscoring the urgent need to improve the robustness of LLM-generated text detection systems. This talk may also introduce some of our other recent works on trustworthy and ethical LLMs.
Biography: Yihan is Ph.D. student at UCLA in Computer Science. She received her B.Eng. degree in Computer Science and Technology from Tsinghua University in June 2020. Ms. Wang's research interest is machine learning, especially improving trustworthiness and generalization of machine learning models. Yihan is currently working with Prof. Cho-Jui Hsieh at UCLA. If speaker approves to be recorded for this NL Seminar talk, it will be posted on our USC/ISI YouTube page within 1-2 business days: https://www.youtube.com/user/USCISI. Subscribe here to learn more about upcoming seminars: https://www.isi.edu/events/
Host: Jon May and Justin Cho
More Info: https://nlg.isi.edu/nl-seminar/
Webcast: https://youtu.be/Fx1T9lyNDh0?si=qEL0QipveladKDwPLocation: Information Science Institute (ISI) - Virtual and ISI-Conf Rm#689
WebCast Link: https://youtu.be/Fx1T9lyNDh0?si=qEL0QipveladKDwP
Audiences: Everyone Is Invited
Contact: Pete Zamar
Event Link: https://nlg.isi.edu/nl-seminar/
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Get To Know Apple Virtual Event
Thu, Feb 22, 2024 @ 12:00 PM - 01:00 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Get To Know Apple Virtual Event
Thursday, February 22, 2024
12:00 p.m. - 1:00 p.m. PST
Virtual - RSVP Required - RSVP HERE: https://joinapple.avature.net/GetToKnowAppleVirtualEvent
Description:
There’s a place at Apple for every kind of brilliant. Our differences are our greatest strengths, leading to the collaboration and innovation that allow you to do the best work of your life.
During this event, you’ll learn more about Apple, employment opportunities for students, and hear firsthand from recruiters what it takes to stand out during the selection and interview process. Apple has invited a select group of universities across the nation to attend this event.
You must RSVP if you’re planning to attend. The link to join will be sent out 24 hours before the event.Location: Virtual Event
Audiences: Everyone Is Invited
Contact: RTH 218 Viterbi Career Connections
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Leveraging LinkedIn to Network Virtually
Thu, Feb 22, 2024 @ 01:00 PM - 02:00 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
THIS EVENT WILL BE HOSTED HYBRID: IN-PERSON & ONLINE SIMULTANEOUSLY
This event is designed to help you learn how to network virtually on LinkedIn. You will learn how to draft an introductory message, how to find and research industry professionals, and how to convert your contacts into potential mentee/mentor relationships.
Zoom link: https://usc.zoom.us/meeting/register/tJAtf-2trTwiG9L-XKIegqBHKtaTYQUH3eZ7
For more information about all workshops, please visit viterbicareers.usc.edu/workshops.Location: Ronald Tutor Hall of Engineering (RTH) - 211
Audiences: All Viterbi
Contact: RTH 218 Viterbi Career Connections
Event Link: https://usc.zoom.us/meeting/register/tJAtf-2trTwiG9L-XKIegqBHKtaTYQUH3eZ7
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Boeing Freshman Design Challenge
Thu, Feb 22, 2024 @ 06:30 PM - 07:30 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Attend this information session to learn about the Boeing Freshman Design Challenge!
During the Info Session: Learn about the Challenge, the agenda, and teams, and receive a signup link to Create or join a team.
Date: 02/22/2024
Time: 6:30 pm - 7:30 pm
Register for this virtual info session on Zoom HERE.
Target student audience: Freshman, any Viterbi Major is invited!
About the freshman Design Challenge: During this unique, resume-building experience, students will also have the opportunity to network with Boeing engineers and executives, who will be available to act as mentors and judges. Also, Representatives from Boeing’s campus team will share upcoming recruitment opportunitiesLocation: Virtual Event
Audiences: Everyone Is Invited
Contact: RTH 218 Viterbi Career Connections
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EiS Communications Hub Drop-In Hours
Fri, Feb 23, 2024 @ 10:00 AM - 01:00 PM
Viterbi School of Engineering Student Affairs
Workshops & Infosessions
Viterbi Ph.D. students are invited to stop by the EiS Communications Hub for one-on-one instruction for their academic and professional communications tasks. All instruction is provided by Viterbi faculty at the Engineering in Society Program.
Location: Ronald Tutor Hall of Engineering (RTH) - 222A
Audiences: Viterbi Ph.D. Students
Contact: Helen Choi
Event Link: https://sites.google.com/usc.edu/eishub/home?authuser=0
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EiS Communications Hub Drop-In Hours
Fri, Feb 23, 2024 @ 10:00 AM - 01:00 PM
Engineering in Society Program
Student Activity
Drop-in hours for writing and speaking support for Viterbi Ph.D. students
Location: Ronald Tutor Hall of Engineering (RTH) - 222
Audiences: Everyone Is Invited
Contact: Helen Choi
Event Link: https://sites.google.com/usc.edu/eishub/home
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Alfred E. Mann Department of Biomedical Engineering
Fri, Feb 23, 2024 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Peter Chung, Ph.D., Robert D. Beyer Early Career Chair in the Natural Sciences and an assistant professor in the Department of Physics and Astronomy University of Southern California
Talk Title: Polymers and Parkinsons: Elucidating Protein Function through Soft Matter Paradigms and Techniques
Abstract: Despite being unequivocally linked to Parkinson’s disease, the function of alpha-synuclein remains unclear beyond transiently binding to the lipid membrane of synaptic vesicles (organelles filled with neurotransmitters). This is due, in part, to its intrinsically disordered nature; alpha-synuclein does not fold into a globular structure and instead behaves much like a biopolymer. While precluding traditional characterization methods, this makes alpha-synuclein incredibly amenable to investigation via a polymer physics framework. First, through purpose-designed membrane nanoparticles and advanced synchrotron X-ray methods I will demonstrate that alpha-synuclein binds to and collectively works to sterically-stabilize membrane surfaces, a biological manifestation of polyelectrolyte-stabilized colloids. I will then reconcile observed transient binding to synaptic vesicles by establishing that alpha-synuclein preferentially binds to osmotically-stressed membranes (a proxy for neurotransmitter-filled synaptic vesicles), a newly discovered biophysical function by which alpha-synuclein interrogates organelle contents. Utilizing these insights, I will contextualize alpha-synuclein as a guidepost that spatiotemporally directs non-equilibrium
Biography: Peter Chung is the Robert D. Beyer Early Career Chair in the Natural Sciences and an assistant professor in the Department of Physics and Astronomy at the University of Southern California. His research focuses on the intersection of intrinsically disordered proteins (especially those unequivocally linked to neurodegenerative disease) and soft matter physics, with the hope of understanding emergent phenomena associated with these proteins and repurposing them for basic science research and novel therapeutic approaches. Previously he was a Kadanoff-Rice Postdoctoral Fellow at the University of Chicago and earned his PhD from the University of California, Santa Barbara
Host: Eunji Chung
Location: Olin Hall of Engineering (OHE) - 100 B
Audiences: Everyone Is Invited
Contact: Carla Stanard
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CSC/CommNetS-MHI Seminar: Milad Siami
Fri, Feb 23, 2024 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Milad Siami, Assistant Professor of Electrical and Computer Engineering | Northeastern University
Talk Title: Optimizing sparse interactions for control and sensing in complex networks
Series: CSC/CommNetS-MHI Seminar Series
Abstract:
This presentation introduces innovative strategies for enhancing control and sensing in large- scale complex networks, with a focus on minimizing resource usage to improve system performance. We address the challenge of non-submodular sensor scheduling in large-scale linear time-varying dynamics, tackling combinatorial, non-convex, NP-hard tasks. Beginning with a simple greedy algorithm, we present an approximation bound based on submodularity and curvature concepts, showing its superiority over existing methods. Shifting to discrete-time autonomous vehicle platoons, we employ graph- theoretic principles for state feedback laws, analyzing stability conditions based on underlying graph properties and update cycles. We explore H2-based robustness, demonstrating the impact of network density and update cycles on system performance. Specifically, we show that denser networks (i.e., networks with more communication links) might require faster agents (i.e., smaller update cycles) to outperform or achieve the same level of robustness as sparse networks (i.e., networks with fewer communication links). Practical examples and results from simulations and experiments, including work with Quanser's Qlabs and Qcars, validate the effectiveness of our approaches, emphasizing strategic sensor scheduling and robust design in autonomous vehicle platoons.
Biography:
Milad Siami is an Assistant Professor in the Department of Electrical and Computer Engineering at Northeastern University and a Core Faculty Member of the Institute for Experiential AI at the same institution. Prior to joining Northeastern, he served as a Postdoctoral Associate at the MIT Institute for Data, Systems, and Society. He earned his M.Sc. and Ph.D. degrees in Mechanical Engineering from Lehigh University and was a long- term visiting researcher at the Institute for Mathematics and Its Applications at the University of Minnesota. Additionally, he has experience as a Software Engineering Research Intern in the Modeling and Data Mining Group at Google Research NYC. Dr. Siami's research primarily focuses on the structural/graphical underpinnings of large-scale
dynamical networks and enhancing the reliability and security of AI-based autonomous systems. His specific areas of interest include distributed control systems, multi-robot systems, and autonomous networks. His current research is supported by grants from the National Science Foundation (NSF), the Department of Homeland Security (DHS), the Office of Naval Research (ONR), and the Army Research Laboratory (ARL).
Host: Dr. Mihailo Jovanovic
More Info: https://csc.usc.edu/seminars/2024Spring/siami.html
More Information: 2024.02.23 Seminar - Milad Siami.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 132
Audiences: Everyone Is Invited
Contact: Miki Arlen
Event Link: https://csc.usc.edu/seminars/2024Spring/siami.html
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CSC/CommNetS-MHI Seminar: Milad Siami
Fri, Feb 23, 2024 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Milad Siami, Assistant Professor of Electrical and Computer Engineering | Northeastern University
Talk Title: Optimizing sparse interactions for control and sensing in complex networks
Series: CSC/CommNetS-MHI Seminar Series
Abstract:
This presentation introduces innovative strategies for enhancing control and sensing in large- scale complex networks, with a focus on minimizing resource usage to improve system performance. We address the challenge of non-submodular sensor scheduling in large-scale linear time-varying dynamics, tackling combinatorial, non-convex, NP-hard tasks. Beginning with a simple greedy algorithm, we present an approximation bound based on submodularity and curvature concepts, showing its superiority over existing methods. Shifting to discrete-time autonomous vehicle platoons, we employ graph- theoretic principles for state feedback laws, analyzing stability conditions based on underlying graph properties and update cycles. We explore H2-based robustness, demonstrating the impact of network density and update cycles on system performance. Specifically, we show that denser networks (i.e., networks with more communication links) might require faster agents (i.e., smaller update cycles) to outperform or achieve the same level of robustness as sparse networks (i.e., networks with fewer communication links). Practical examples and results from simulations and experiments, including work with Quanser's Qlabs and Qcars, validate the effectiveness of our approaches, emphasizing strategic sensor scheduling and robust design in autonomous vehicle platoons.
Biography:
Milad Siami is an Assistant Professor in the Department of Electrical and Computer Engineering at Northeastern University and a Core Faculty Member of the Institute for Experiential AI at the same institution. Prior to joining Northeastern, he served as a Postdoctoral Associate at the MIT Institute for Data, Systems, and Society. He earned his M.Sc. and Ph.D. degrees in Mechanical Engineering from Lehigh University and was a long- term visiting researcher at the Institute for Mathematics and Its Applications at the University of Minnesota. Additionally, he has experience as a Software Engineering Research Intern in the Modeling and Data Mining Group at Google Research NYC. Dr. Siami's research primarily focuses on the structural/graphical underpinnings of large-scale dynamical networks and enhancing the reliability and security of AI-based autonomous systems. His specific areas of interest include distributed control systems, multi-robot systems, and autonomous networks. His current research is supported by grants from the National Science Foundation (NSF), the Department of Homeland Security (DHS), the Office of Naval Research (ONR), and the Army Research Laboratory (ARL).
Host: Dr. Mihailo Jovanovic
More Info: https://csc.usc.edu/seminars/2024Spring/siami.html
More Information: 2024.02.23 Seminar - Milad Siami.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 132
Audiences: Everyone Is Invited
Contact: Miki Arlen
Event Link: https://csc.usc.edu/seminars/2024Spring/siami.html
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CSC/CommNetS-MHI Seminar: Milad Siami
Fri, Feb 23, 2024 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Milad Siami, Assistant Professor of Electrical and Computer Engineering | Northeastern University
Talk Title: Optimizing sparse interactions for control and sensing in complex networks
Series: CSC/CommNetS-MHI Seminar Series
Abstract:
This presentation introduces innovative strategies for enhancing control and sensing in large- scale complex networks, with a focus on minimizing resource usage to improve system performance. We address the challenge of non-submodular sensor scheduling in large-scale linear time-varying dynamics, tackling combinatorial, non-convex, NP-hard tasks. Beginning with a simple greedy algorithm, we present an approximation bound based on submodularity and curvature concepts, showing its superiority over existing methods. Shifting to discrete-time autonomous vehicle platoons, we employ graph- theoretic principles for state feedback laws, analyzing stability conditions based on underlying graph properties and update cycles. We explore H2-based robustness, demonstrating the impact of network density and update cycles on system performance. Specifically, we show that denser networks (i.e., networks with more communication links) might require faster agents (i.e., smaller update cycles) to outperform or achieve the same level of robustness as sparse networks (i.e., networks with fewer communication links). Practical examples and results from simulations and experiments, including work with Quanser's Qlabs and Qcars, validate the effectiveness of our approaches, emphasizing strategic sensor scheduling and robust design in autonomous vehicle platoons.
Biography:
Milad Siami is an Assistant Professor in the Department of Electrical and Computer Engineering at Northeastern University and a Core Faculty Member of the Institute for Experiential AI at the same institution. Prior to joining Northeastern, he served as a Postdoctoral Associate at the MIT Institute for Data, Systems, and Society. He earned his M.Sc. and Ph.D. degrees in Mechanical Engineering from Lehigh University and was a long- term visiting researcher at the Institute for Mathematics and Its Applications at the University of Minnesota. Additionally, he has experience as a Software Engineering Research Intern in the Modeling and Data Mining Group at Google Research NYC. Dr. Siami's research primarily focuses on the structural/graphical underpinnings of large-scale dynamical networks and enhancing the reliability and security of AI-based autonomous systems. His specific areas of interest include distributed control systems, multi-robot systems, and autonomous networks. His current research is supported by grants from the National Science Foundation (NSF), the Department of Homeland Security (DHS), the Office of Naval Research (ONR), and the Army Research Laboratory (ARL).
Host: Dr. Mihailo Jovanovic
More Information: 2024.02.23 Seminar - Milad Siami.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 132
Audiences: Everyone Is Invited
Contact: Miki Arlen
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CSC/CommNetS-MHI Seminar: Milad Siami
Fri, Feb 23, 2024 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Milad Siami, Assistant Professor of Electrical and Computer Engineering | Northeastern University
Talk Title: Optimizing sparse interactions for control and sensing in complex networks
Series: CSC/CommNetS-MHI Seminar Series
Abstract:
This presentation introduces innovative strategies for enhancing control and sensing in large- scale complex networks, with a focus on minimizing resource usage to improve system performance. We address the challenge of non-submodular sensor scheduling in large-scale linear time-varying dynamics, tackling combinatorial, non-convex, NP-hard tasks. Beginning with a simple greedy algorithm, we present an approximation bound based on submodularity and curvature concepts, showing its superiority over existing methods. Shifting to discrete-time autonomous vehicle platoons, we employ graph- theoretic principles for state feedback laws, analyzing stability conditions based on underlying graph properties and update cycles. We explore H2-based robustness, demonstrating the impact of network density and update cycles on system performance. Specifically, we show that denser networks (i.e., networks with more communication links) might require faster agents (i.e., smaller update cycles) to outperform or achieve the same level of robustness as sparse networks (i.e., networks with fewer communication links). Practical examples and results from simulations and experiments, including work with Quanser's Qlabs and Qcars, validate the effectiveness of our approaches, emphasizing strategic sensor scheduling and robust design in autonomous vehicle platoons.
Biography:
Milad Siami is an Assistant Professor in the Department of Electrical and Computer Engineering at Northeastern University and a Core Faculty Member of the Institute for Experiential AI at the same institution. Prior to joining Northeastern, he served as a Postdoctoral Associate at the MIT Institute for Data, Systems, and Society. He earned his M.Sc. and Ph.D. degrees in Mechanical Engineering from Lehigh University and was a long- term visiting researcher at the Institute for Mathematics and Its Applications at the University of Minnesota. Additionally, he has experience as a Software Engineering Research Intern in the Modeling and Data Mining Group at Google Research NYC. Dr. Siami's research primarily focuses on the structural/graphical underpinnings of large-scale dynamical networks and enhancing the reliability and security of AI-based autonomous systems. His specific areas of interest include distributed control systems, multi-robot systems, and autonomous networks. His current research is supported by grants from the National Science Foundation (NSF), the Department of Homeland Security (DHS), the Office of Naval Research (ONR), and the Army Research Laboratory (ARL).
Host: Dr. Mihailo Jovanovic, mihailo@usc.edu
More Info: https://csc.usc.edu/seminars/2024Spring/siami.html
More Information: 2024.02.23 Seminar - Milad Siami.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 132
Audiences: Everyone Is Invited
Contact: Miki Arlen
Event Link: https://csc.usc.edu/seminars/2024Spring/siami.html
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CSC/CommNetS-MHI Seminar: Milad Siami
Fri, Feb 23, 2024 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Milad Siami, Assistant Professor of Electrical and Computer Engineering | Northeastern University
Talk Title: Optimizing sparse interactions for control and sensing in complex networks
Series: CSC/CommNetS-MHI Seminar Series
Abstract:
This presentation introduces innovative strategies for enhancing control and sensing in large- scale complex networks, with a focus on minimizing resource usage to improve system performance. We address the challenge of non-submodular sensor scheduling in large-scale linear time-varying dynamics, tackling combinatorial, non-convex, NP-hard tasks. Beginning with a simple greedy algorithm, we present an approximation bound based on submodularity and curvature concepts, showing its superiority over existing methods. Shifting to discrete-time autonomous vehicle platoons, we employ graph- theoretic principles for state feedback laws, analyzing stability conditions based on underlying graph properties and update cycles. We explore H2-based robustness, demonstrating the impact of network density and update cycles on system performance. Specifically, we show that denser networks (i.e., networks with more communication links) might require faster agents (i.e., smaller update cycles) to outperform or achieve the same level of robustness as sparse networks (i.e., networks with fewer communication links). Practical examples and results from simulations and experiments, including work with Quanser's Qlabs and Qcars, validate the effectiveness of our approaches, emphasizing strategic sensor scheduling and robust design in autonomous vehicle platoons.
Biography:
Milad Siami is an Assistant Professor in the Department of Electrical and Computer Engineering at Northeastern University and a Core Faculty Member of the Institute for Experiential AI at the same institution. Prior to joining Northeastern, he served as a Postdoctoral Associate at the MIT Institute for Data, Systems, and Society. He earned his M.Sc. and Ph.D. degrees in Mechanical Engineering from Lehigh University and was a long- term visiting researcher at the Institute for Mathematics and Its Applications at the University of Minnesota. Additionally, he has experience as a Software Engineering Research Intern in the Modeling and Data Mining Group at Google Research NYC. Dr. Siami's research primarily focuses on the structural/graphical underpinnings of large-scale dynamical networks and enhancing the reliability and security of AI-based autonomous systems. His specific areas of interest include distributed control systems, multi-robot systems, and autonomous networks. His current research is supported by grants from the National Science Foundation (NSF), the Department of Homeland Security (DHS), the Office of Naval Research (ONR), and the Army Research Laboratory (ARL).
Host: Dr. Mihailo Jovanovic, mihailo@usc.edu
More Info: https://csc.usc.edu/seminars/2024Spring/siami.html
More Information: 2024.02.23 Seminar - Milad Siami.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 132
Audiences: Everyone Is Invited
Contact: Miki Arlen
Event Link: https://csc.usc.edu/seminars/2024Spring/siami.html
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ECE Seminar
Fri, Feb 23, 2024 @ 03:30 PM - 04:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Jorge F. Silva, PhD, Universidad de Chile
Talk Title: Information Theoretic Measures for Representation Learning
Abstract: Information-theoretic measures have been widely adopted for machine learning (ML) feature design. Inspired by this, we look at the relationship between information loss in the Shannon sense and the operation loss in the minimum probability of error (MPE) sense when considering a family of lossy representations (or encoders). In this talk, we introduce a series of results that show how adequate the adoption of mutual information (MI) is for predicting the operational quality of a representation in classification. Our findings support the observation that selecting/designing representations that capture informational sufficiency (IS) is appropriate for learning. However, we also show that this selection is rather conservative if the intended goal is achieving MPE in classification. We conclude by discussing the capacity of the information bottleneck (IB) method to achieve lossless prediction and the expressive power of digital encoders in ML.
Biography: Information-theoretic measures have been widely adopted for machine learning (ML) feature design. Inspired by this, we look at the relationship between information loss in the Shannon sense and the operation loss in the minimum probability of error (MPE) sense when considering a family of lossy representations (or encoders). In this talk, we introduce a series of results that show how adequate the adoption of mutual information (MI) is for predicting the operational quality of a representation in classification. Our findings support the observation that selecting/designing representations that capture informational sufficiency (IS) is appropriate for learning. However, we also show that this selection is rather conservative if the intended goal is achieving MPE in classification. We conclude by discussing the capacity of the information bottleneck (IB) method to achieve lossless prediction and the expressive power of digital encoders in ML.
Host: Dr. Eduardo Pavez
More Information: Jorge Silva Seminar 2.23.24.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Gloria Halfacre
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KIUEL: L'Oreal Brandstorm Demo Day (National Engineers Day)
Fri, Feb 23, 2024 @ 06:00 PM - 09:00 PM
USC Viterbi School of Engineering
Receptions & Special Events
The competition finale hosted by the Society of Cosmetic Chemists and KIUEL. Follow KIUELâs Instagram for updates!
Location: Sign into EngageSC to View Location
Audiences:
Contact: Kevin Giang
Event Link: https://engage.usc.edu/viterbi/rsvp?id=395824
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